Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

dc.contributor.author	Σταθακοπούλου, Ρεγγίνα	el
dc.contributor.author	Μαγούλας, Γεώργιος Δ.	el
dc.contributor.author	Γρηγοριάδου, Μαρίλια	el
dc.contributor.author	Σαμαράκου, Μαρία	el
dc.date.accessioned	2015-04-26T09:44:57Z
dc.date.available	2015-04-26T09:44:57Z
dc.date.issued	2015-04-26
dc.identifier.uri	http://hdl.handle.net/11400/8970
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/	en
dc.source	http://www.sciencedirect.com/science/article/pii/S0020025504000702#	en
dc.subject	Student diagnosis
dc.subject	Uncertainty management
dc.subject	Διάγνωση φοιτητών
dc.subject	Διαχείρισης αβεβαιότητας
dc.title	Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Computer programming
heal.classification	Τεχνολογία
heal.classification	Προγραμματισμός
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://skos.um.es/unescothes/C00749
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Προγραμματισμός
heal.identifier.secondary	doi:10.1016/j.ins.2004.02.026
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2005
heal.bibliographicCitation	Stathacopoulou, R., Magoulas, G., Grigoriadou, M. and Samarakou, M. (February 2005). Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis. Information Sciences. 170(2-4). pp. 273-307. Elsevier: 2005. Available from: http://www.sciencedirect.com/science/article/pii/S0020025504000702	en
heal.abstract	In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent “imitate” teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments.	en
heal.publisher	Elsevier	en
heal.journalName	Information Sciences	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false